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Anthropic Raises $65B in Series H Funding at $965B Post-Money Valuation

Anthropic Raises $65B in Series H Funding at $965B Post-Money Valuation

· By Mansa Muhammad

Anthropic has secured $65 billion in Series H funding, bringing the company to a $965 billion post-money valuation according to the company's Series H announcement. The round was led by Altimeter Capital, Dragoneer, Greenoaks, and Sequoia Capital.

This capital injection follows a period of rapid scaling. Since the company's Series G in February, run-rate revenue crossed $47 billion earlier this month. The funding includes $15 billion of previously committed investments from hyperscalers, which includes $5 billion from Amazon.

The scale of this investment signals a shift from experimental AI to industrial-scale deployment. Anthropic is positioning itself to meet what its CFO, Krishna Rao, describes as historic demand. The capital is earmarked for advancing safety and interpretability research, expanding compute capacity, and scaling products and partnerships.

The composition of the cap table reveals the deepening integration between frontier model labs and the global financial and infrastructure ecosystem. Beyond traditional venture firms like Capital Group, Coatue, and D1 Capital Partners, the round includes significant participation from entities such as Blackstone, Fidelity Management & Research Company, and Temasek.

The strategic importance of the hardware layer is also evident. Anthropic is working with infrastructure partners including Micron, Samsung, and SK hynix to secure the memory, storage, and logic chips necessary for scaling. To support the compute requirements of Claude, the company has signed agreements for new capacity with Amazon, Google, and Broadcom, alongside access to GPU capacity in Colossus 1 and Colossus 2 via SpaceX.

This level of capitalization suggests that the frontier model race is no longer just about algorithmic breakthroughs, but about the ability to command massive amounts of energy and silicon. As Anthropic expands its compute capacity, the bottleneck for AI progress moves from software ingenuity to the physical limits of power and hardware supply chains.

The question for the industry is whether the massive capital deployment can keep pace with the escalating energy and infrastructure requirements of next-generation models.

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